code: def test_conv_model(self):
      num_classes = 10
      input_shape = (1, 28, 28, 1)
      input_data = np.random.normal(size=input_shape)
  
      model = tf.keras.models.Sequential()
      model.add(
          tf.keras.layers.Conv2D(
              2, (3, 3), batch_input_shape=input_shape
          )
      )
      model.add(tf.keras.layers.ReLU())
      model.add(tf.keras.layers.BatchNormalization())
      model.add(tf.keras.layers.AveragePooling2D((2, 2)))
      model.add(tf.keras.layers.Conv2D(2, (3, 3)))
      model.add(tf.keras.layers.ReLU())
      model.add(tf.keras.layers.BatchNormalization())
      model.add(tf.keras.layers.AveragePooling2D((2, 2)))
      model.add(tf.keras.layers.Flatten())
      model.add(tf.keras.layers.Dense(num_classes, name="logit"))
  
      expected = model.predict(input_data)
      k_weights = model.get_weights()
      k_config = model.get_config()
  
      with tf.name_scope("TFE"):
          x = tfe.define_private_input(
              "inputter", lambda: tf.convert_to_tensor(input_data)
          )
  
          tfe_model = tfe.keras.models.model_from_config(k_config)
  
          tfe_model.set_weights(k_weights)
          y = tfe_model(x)
          actual = y.reveal().to_native()
  
          np.testing.assert_allclose(
              actual, expected, rtol=1e-2, atol=1e-2
          )
  
file_path: tf-encrypted/tf_encrypted/keras/models/sequential_test.py
imports: import unittest
  import numpy
  import tensorflow
  import tf_encrypted
  from tf_encrypted.keras import Sequential
  from tf_encrypted.keras.layers import Dense
label_desc: Use image node_id 6 for calling the TestSequential obj's underlying member method code with example usage: obj.test_conv_model() without return types